Score Personal Loan Applicants using R
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Score Personal Loan Applicants using R

A Step-by-Step Guide to Doing Predictive Analysis Using Logistic Regression and R

About the Book

This book is for you if

  • you've been building scorecards using SAS or SPSS and want to do the same thing in R, or
  • you want a step-by-step guide to building and backtesting a binary classifier using logistic regression in R, or
  • you want to learn how to automate the model selection process using the best subset mehtod, or
  • you want to understand the differences amongst the 6 performance measures: Accuracy, Sensitivity, False Positive Rate, Specificity, Precision, and the F-measure, and when to use which.
  • you want to learn how to make the ROC curve and calculate the Aear Under the Curve (AUC) in R.

This book is not for you if

  • you're looking for thorough understanding of the relavent statistical theories. This is a how-to book, not a why book, though intuitive reasons are pointed out whenever possible.
  • you need to see math formulas. All formulas are presented directly in the form of R code.
  • you're looking for an introductory R book. This book doesn't explain how R works. 

About the Author

Guangming Lang
Guangming Lang

Guangming Lang is the founder and Chief Data Scientist at Cabaceo LLC. He also teaches people how to do data analysis using the R programming language at, where you can receive high quality training materials for free. He used to work for FICO. He has a masters degree in biostatistics from the University of Michigan and a bachelor's degree in mathematics from the New College of Florida. 


Table of Contents

  • Preface
  • Introduction
  • 1. Set up
  • 2. Preliminary Analysis
    • 2.1 Clean Data
    • 2.2 Descriptive Analysis
    • 2.3 Weight of Evidence and Information Value
    • 2.4 Simple Logit Models
    • 2.5 Pre-select Predictors
    • 2.6 Correlations Amongst Predictors
  • 3. Predictive Analysis
    • 3.1 Model Selection Using the Best Subsets Algorithm
    • 3.2 Backtesting
    • 3.3 Performance Curves
    • 3.4 Cross Validation
    • 3.5 Final Model
  • Conclusion

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